Today, large numbers of computing devices are connected to each other over the Internet and similar types of public and private computer networks. In various fields, those computing devices can control and monitor the operation of equipment, appliances, and devices in manufacturing facilities, for infrastructure systems, for automobiles, in residential, commercial, and medical environments, and in other applications. Depending upon the field, those computing devices collect various types of data, such as computing resource usage data, manufacturing parameter data, infrastructure usage data, activity level data, and location data, among other types.
Thus, various types of data can be collected by computing devices, and that data can be communicated over computer networks, stored, and analyzed to determine trends and identify problems. Particularly, as relatively larger datasets are collected from computing devices, those data sets can be analyzed computationally to reveal patterns, trends, and associations.
In the context of relatively large datasets, anomaly detection is related to the identification of data values that do not conform to an expected range in a dataset. Sometimes, an anomalous data value can correspond to some kind of problem, such as bank fraud, a structural defect, a medical problem, or other error or fault. Anomalies can also be referred to as outliers, novelties, noise, deviations, and exceptions.